Towards a Unified Model of Knowledge Acquisition and Linking


Towards a Unified Model of Knowledge Acquisition and Linking – Many problems in the knowledge transfer and related areas are complex due to the lack of sufficient training data for the tasks. For a given dataset, researchers make use of a collection of annotated training datasets to train a model that is trained to extract the relevant knowledge from any annotated target dataset. In this paper, we consider the problem of inferring the most relevant information from the training data using a deep neural network (DNN) to predict semantic classes of data for an annotated label (n=2). We first evaluate the DNN model in a semantic class by a simple regression task. We show that as the discriminative model learns to infer the most relevant category predictions, it outperforms the state-of-the-art models.

We present a novel method of identifying the semantic content of a text using a novel semantic segmentation criterion that has been developed over three decades of existing works. Our key feature is the use of a new nonlinear feature, called the nonconvex feature, that is equivalent to a Gaussian process feature over the vector space. To further improve the performance of our approach we propose a novel approach based on a novel nonlinear parameterizing factor of a multi-dimensional Euclidean distance to a prior distribution of the text as an output. As we show in the paper, our model converges significantly faster than the state of the art techniques, despite not being able to directly scale to small text sizes. We also demonstrate the efficacy of our approach on several different data sets, including four languages.

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Towards a Unified Model of Knowledge Acquisition and Linking

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    Detecting and resolving conflicts in multiword e-mail messagesWe present a novel method of identifying the semantic content of a text using a novel semantic segmentation criterion that has been developed over three decades of existing works. Our key feature is the use of a new nonlinear feature, called the nonconvex feature, that is equivalent to a Gaussian process feature over the vector space. To further improve the performance of our approach we propose a novel approach based on a novel nonlinear parameterizing factor of a multi-dimensional Euclidean distance to a prior distribution of the text as an output. As we show in the paper, our model converges significantly faster than the state of the art techniques, despite not being able to directly scale to small text sizes. We also demonstrate the efficacy of our approach on several different data sets, including four languages.


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